Private Firm Commercial & Industrial Credit Risk Solutions & Best Practices

Private Firm Commercial & Industrial (C&I) Credit Risk Solutions & Best Practices

JUNE 2014 MEHNA RAISSI, DIRECTOR, PRODUCT MANAGEMENT CHRISTIAN HENKEL, DIRECTOR, RISK CONSULTING

Private Firm Commercial & Industrial (C&I) Credit Risk Solutions & Best Practices June 2014

Speakers

Mehna Raissi is a Director in Product Management in the Enterprise Risk Solutions group with Moody’s Analytics and has been with the firm for nearly six years. She manages the single obligor credit risk products suite which include RiskCalc, Commercial Mortgage Metrics and LossCalc. Mehna is responsible for the management and product innovation of these premier credit risk management tools. Mehna completed her Bachelors in Managerial Economics from University of California, Davis, and her MBA from University of San Francisco.

Chris Henkel is a Director in the Enterprise Risk Solutions group with Moody’s Analytics where he leads the risk measurement delivery team throughout the Americas. He has vast experience offering advisory services and custom quantitative risk solutions to clients. Chris has served as a credit risk instructor and is a frequent lecturer in industry conferences and organizations. He received his master’s and undergraduate degree from the University of Texas and graduated Valedictorian form the Southwestern Graduate School of Banking at Southern Methodist University.


Agenda

1. Credit Risk Management Challenges

2. Best Practices

3. Stress Testing Model and Approach

4. Private Firm C&I Risk Tools

5. Questions


C&I Credit Risk Management Challenges 1


Challenges in Private Firm C&I Risk Management

Data Quality & Availability

What is the data quality?

Standardized Processes

Ongoing Monitoring

Other Risk Drivers

Credit Risk Models

• Limited up to date data and ongoing availability

• Data captured at origination may not be complete for ongoing data analysis

• Data management is important for historical and forward looking analysis

• Storing data in a single system of record for consistency

• Improving operational controls by standardizing credit policies

• Setting up workflow processes to ensure systematic loan origination processes

• Improve credit origination decisions with accurate and predictive risk models

• Leveraging risk models for capital allocation and reserve setting

• Stress testing models that leverages baseline borrower risk

• Early warning indictor of risk deteriorations

• Dashboard reports showing borrower risk migration

• Setting limits based on risk levels

• Understand unexpected shifts that provide additional transparency

• Incorporate qualitative factors for a comprehensive analysis

How to minimize errors?

What are the most effective credit risk

tools?

How to manage counter-party risk?

What other factors should be taken into

consideration?

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Managing the multiple dimensions to stress testing

Stress Testing

Regulatory Requirements

Firm Goals

Primary, Challenger & Benchmark

Model

Customization Methodology “Bottom-up

vs. Top-down”

Asset Classes

Data Availability &

Quality

6


C&I Best Practices 2


» Combine financial spreading and credit analysis in one platform

» Stores all data in a single system of record

» Improves credit origination decisions across all asset classes

» Improves operational controls by standardizing credit policies

» Utilize credit risk models for underwriting and monitoring

» Incorporate internal rating models

Importance of statement spreading & dual risk rating


Identify issues before they arise thru ongoing monitoring

» Understand risks in your portfolio within specific segments – View a single borrower’s

performance for specific groups across your portfolio

– Monitor over time for an early warning indicator and an effective approach toward risk rating

– Identify outliers in a portfolio and identify key trends and insights within important segments


Risk monitoring and dashboards to pinpoint outliers

» Probability of Default

» Implied Rating

» % Change

» Peer Comparison

» Risk Rating – high to low

» Credit Committee Review

Company Name1-Year EDF

Implied Rating - Moody's

Previous 1-Year EDF

1-Year EDF ChangePrimary Industry

Above 25th pctl

Above Median

Above 75th pctl

Above 90th pctl

company_name ann_edf_1yr edf_1yr_ir_mdy ann_edf_1yr primary_industryma_id-N07067ma_id-N04797ma_id-N04938ma_id-43906ma_id-346091ma_id-89614Jma_id-N05717ma_id-985515ma_id-579489ma_id-09776Jma_id-708160

Enter Identifiers Below:

Current EDF EDF Change Peer Comparison - Current EDF


Setting risk sensitive limits

» Include credit risk in the management of business limits – Pre-qualification module

– Additional due diligence requirements

– Pricing determination

– Collateral requirements

» Streamlines the decision process with clear limits and action plan – Clear approval vs. decline limits

» Provide transparency behind every decision across the organization – Set limits by industry or region

.

Zero Limits

Low Limits

Medium Limits

High Limits

0.02%

35.00%

0.50%

10.00%

2.00%

5.00%

1.00%

0.20%

EDF

0.05%

0.10%

Exposure

June 2014

Incorporating qualitative factors in your credit assessment

Industry/Market Management

Customer Power Experience in Industry

Diversification of Products Financial Reporting and Formal Planning

Risk Management Company Balance Sheet Factors

Years in Relationship Audit Method

Conduct of Account Debtor Risk/Accounts Receivable Risk

Supplier Power Pro-forma Liquidity

Pro-forma interest coverage

June 2014

Comprehensive qualitative overlay structure

Qualitative Overlay

Category 1 Category 2 Category n …

Question 1 Question 2 Question n … Question 1 Question 2 Question n …

• Option 1

• Option 2

• Option n

• Option 1

• Option 2

• Option n

• Option 1

• Option 2

• Option n

• Option 1

• Option 2

• Option n

• Option 1

• Option 2

• Option n

• Option 1

• Option 2

• Option n

Qualitative Score Quantitative Score

Combined PD


Different modelling approaches to meet stress testing needs

Top-Down » Inputs:

— Initial PD & LGD — Sector — Debt type — Outstanding Loan Balance — Total Commitment — Macro scenarios

» Modeling: — Forecasting future change based on PD

level — Predict recovery rates based on debt

type — Outputs: Stressed PD & LGD, expected

loss, charge offs, EAD, portfolio balance, usage

Bottom-Up

» Inputs: — Income Statement & Balance Sheet

Inputs — Linking to Macro scenarios

» Modeling: — Financial ratios are linked to

macroeconomic variables — Proforma Financials — Outputs: Baseline PDs vs. Stressed PDs


C&I Stress Testing Model and Approach 3


Items Commonly Stressed

» Income (revenues) » Expenses » Rates on interest earning assets » Rates on interest bearing liabilities » Provisions for loan losses » Balances and volumes » Non-performing loans » Charge-offs » RWAs » Capital levels (regulatory and economic) » Capital ratios

Our focus for today is on the loss forecasting components of stress testing

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

Quarterly Charge-Off Rates: C&I Loans (1985-2014)

Source: Federal Reserve, All Banks, NSA; NBER


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%LLP/NOR* LLP/PPNR**

During stressed economic times, the provisions for loan losses consumes a considerable amount of revenues

37.9%

99.1%

Source: FDIC (all insured institutions); NOR = Net Interest Margin + Noninterest Income. PPNR = Net Interest Margin + Noninterest Income – Non Interest Expense


Top-down approaches to loss estimation seek to estimate the level of NCOs for an aggregated portfolio

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Quarterly Charge-Off Rates for C&I Loans (1991 – 2014)

Source: Federal Reserve


Underwriting standards tend to be a good predictor of charge-offs, at an industry level

0.00

0.50

1.00

1.50

2.00

2.50

3.00

-40

-20

0

20

40

60

80

100

Underwriting Standards Charge-Off Rate (1 yr lag)

Adj R-Squared =80%

Comparison of Industry Underwriting Standards and Charge-Off Rates for C&I Loans

Source: Federal Reserve


A credit transition matrix can be used to estimate stressed PDs from ratings linked to scenarios


Ultimately, our goal is to translate the relationship between scenario conditions into obligor credit risk

Scenario

Δ in Expected

Loss Δ in

10-

yr

Trea

sury

Yie

ld

Δ in

1-y

ear F

ed

Fund

s Ta

rget

Δ in

Cor

e G

oods

C

PI

Δ in

Con

sum

er

Con

fiden

ce

Δ in

Spe

c G

rade

Sp

read

s

Δ in

Non

-Far

m

Biz

Prod

uctiv

ity

Oth

ers

…

S1 ?

S2 ?

S3 ?

S4 ?

S5 ?

Scenario Conditions

External Impacts

Internal Impacts

Financial Impacts

Capital Impacts

Δ in Probability of Default Δ

in 1

0-yr

Tr

easu

ry Y

ield

Δ in

Cor

pora

te

Tax

Rat

e

Δ in

1-y

ear F

ed

Fund

s Ta

rget

Δ in

Cor

e G

oods

C

PI

Δ in

Wag

es a

nd

Sala

ries

Δ in

Con

sum

er

Con

fiden

ce

Δ in

Spe

c G

rade

Sp

read

s

Δ in

Non

-Far

m

Biz

Prod

uctiv

ity

Oth

ers

…

--- … … … … … … … … …

• The macroeconomic variables are often drawn from those specified by the Federal Reserve in the CCAR process

• Banks and the Fed alike use PD, LGD, and EAD models are used to calculate the EL – and translate those to charge-offs at the segment level

• The PD for a C&I loan is projected over the planning horizon by first calculating the PD at the beginning and projecting it forward

• The output is a forecast of obligor-level PDs at each quarter of the forecast horizon under a given scenario

• The CCA EDF (or internal rating) is the starting point for the forecast horizon

HISTORICAL DATA PREDICTIONS (Via regression model)

Independent “explanatory” variables (macroeconomic factors) Regression modeled

Predictions Values of macro factors from

forecast scenarios

iii

i XFactor εβα +∆×+=∆ ∑ ]%[%

Dependent variables (credit risk measures,

such as PD)

FOR ILLUSTRATION


» The dataset was divided (CRD EDF data) into homogenous risk pools

» Sector and credit risk (current EDF bucket) are identified as two main factors which have different exposures and sensitivities to macro variable shocks

» The model is built by assessing impact of macro variables across each sector and EDF bucket

» The model is calibrated across different PD levels using a continuous distribution of PD values

Model Coefficients (Sensitivity to

Macroeconomic Variables)

Credit risk (EDF Bucket)

Sector risk

We’ve developed a “granular” stress testing methodology built upon our CRD and EDF data


The EDF data was divided into sectors and also rating buckets, based upon equally spaced deciles

EDF Rating Buckets RiskCalc Sectors


Macrovariables were selected following the sector and rating segmentation process

1) Group the macroeconomic variables into similar categories (i.e., market, economic, interest rate, RE prices)

2) Univariate estimation (after transformation)

3) For each variable from #2, add a 2nd variable. Repeat with same criteria for all combinations

4) Proceed to the three-variable model (same logic)

5) Stop once the R-Squared cannot be improved, or becomes counterintuitive

6) Test candidate models (sub-sample)

7) Pick the final set

Domestic CCAR Variables


The model’s predicted PD (four-quarter ahead) is closely aligned with the actual mean PD


Similarly, the predicted 9-quarter EDFs were closely aligned with the actual EDFs during the crisis period

Aggregate Sector Level


A “ratio-based” approach is an alternative that links macroeconomic variables and financial ratios

Obs

erve

d d

efau

lt ra

te

Low High

Low

High Liquidity Ratio

Obs

erve

d d

efau

lt ra

te

Low High

Low

High Leverage

• Each level of a ratio is associated with a different default rate

• If the ratio level changes, so does its PD

Percentile Score Percentile Score


» Similar to the “granular” approach, the model is built using domestic Fed CCAR variables, the CRD, and EDFs

» The model can also be applied to the additional scenarios

» Different financial statement inputs behave different under different stress scenarios, which translate into a wide variation in EDFs

» Income statement items (more responsive) are linked to macrovariables and used to generate pro forma financial statements

» The median for each ratio is derived for each year, state, and sector - which is evaluated to assess which are most responsive to key macrovariables (i.e., GDP, Unemployment, etc.)

- Sales Growth

- Cost of Goods Sold (“COGS”)

- SG&A Expenses

- Interest Expense/Total Liabilities

The “ratio-based” model follows a bottom-up approach at the financial statement ratio level

Changes in macrovariables flow to the balance sheet through line items in the income statement


Once the relationship with economic variables is established, a pro-forma income statement is created

Income Statement

Sales/Revenue

-Cost of Goods Sold (COGS)

-Selling, General and Administrative Expense (SGA)

-Depreciation/Amortization (AMORT)

-Other Operating Expense (OthrExp)

Total Operating Profit

+Financial Income

-Interest Expenses

Profit before Tax

-Tax

Net Income

Responds to the Cycle

Responds to Interest Rates

Variable costs such as Cost of Goods Sold move together with changes in

Sales.

Fixed costs, such as Depreciation/Amortization

move slowly when Sales decrease.

Sales Growth COGS Changes

SGA Changes Interest Expense Changes


» To be consistent across the four dependent variable models, we selected macrovariables that are uniform and also statistically significant for the dependent variables (i.e., financial ratios)

» Separate models are fit for each sector, resulting in three groups

- Group 1 (11): Agriculture, Business Products, Business Services, Communication, Consumer Products, HiTech, Mining, Services, Trade, Transportation, and Unassigned

- Group 2 (2): Healthcare and Utilities - Group 3 (1): Construction

The model starts with the four financial ratios to build the pro forma FSO EDF, then adjusts for the credit cycle

Final Set of Macroeconomic Variables

Stressed EDF = F(Pro Forma FSO EDF x CCA Factor)


The response to changes in macroeconomic variables varies by sector

Final Set of Macroeconomic Variables (CCA Factor)

Agriculture & Transportation are sensitive to the WTI Index

Construction is sensitive to the HPI

Unemployment affects all sectors, - albeit differently


The aggregate and predicted Stressed EDF closely follows the time series of the Actual EDF

Example of Sector Level Validation


Forecasting stressed LGDs often involves incorporating macrovariables directly into an LGD model

» Primary data source was Moody’s Default & Recovery Database (“DRD”), which grew out of the Moody’s Annual Default Studies, used to assess ratings’ performance

» We use the DRD to obtain recovery data, sector classifications, loan types. We also supplement the DRD data with PDs from our Public Firm model and a time series of macroeconomic variables (e.g., DJ Index, VIX)

» The macro variables consist of stationary transformations of domestic CCAR variables. These are the same variables used in the PD stress testing model

01020304050607080

Rec

over

y Pr

ice

Actual vs Predicted Avg. Recovery (365-Day Rolling Window) Recovery Price Model Predicted Recovery Price


The loss emergence period is the time from when default occurs until the time when the loss is realized

• The appropriate level of ALLL at the end of a given quarter is generally assumed to be the amount needed to cover projected loan losses over the next four quarters.

• LLPs will equal the projected NCOs for the quarter plus the amount needed for the ALLL to be at an appropriate level at the end of the quarter, which is a function of projected future NCOs. Source: Moody’s

The Fed models project losses in the accrual using detailed loan portfolio data provided by the BHCs on the FR Y-14 report.


With a forecast of quarterly NCOs, we can quantify the provisions to the ALLL and the impact on capital


C&I Risk Tools 4


RiskAnalyst™ software has wide industry coverage for financial statement data collection needs

» Minimize data entry errors by using industry specific data templates

» Meet your specific business objectives with the flexibility to change templates or add new templates

» Integrate with credit risk assessment models

» Utilize off the shelf or customized internal rating models

Data


RiskCalc Plus Global Presence: Network of 29 World-Class Models

The RiskCalc Plus network is comprised of unique models covering:

Americas: USA, Canada and Mexico country models, plus U.S. Insurance, U.S. Banks and North America Large Firm

Europe, Middle East and Africa: Austria, France, Netherlands, Nordic (Denmark, Norway, Sweden, Finland), Portugal, Spain, UK, Germany, Belgium, Italy, South Africa, Switzerland, Russia, Banks Asia Pacific: Japan, Korea, Australia, Singapore, China, Banks Other: Emerging Markets

12 Million Unique Private Firms

50 Million Financial

Statements 800,000 Defaults

Worldwide

RiskCalc™: Credit Research Database (CRD™) The largest financial statement and default database in the world


Collect Financials and Default Data

Select Relevant Ratios

Compute the Model Output

Calibrate the Model Output to Actual Defaults: Financial Statement Only (FSO) EDF™ (Expected Default Frequency)

Incorporate a market signal to determine the Credit Cycle Adjusted (CCA) EDF

1

2

3

4

5

RiskCalc Modeling Process


Expected Default Frequency (EDF) - Output

40

EDF Credit Measure is in the highest percentile and

mapped to the most risky implied rating


Relative Contributions depict risk drivers

41

Ratio drivers point out many weaknesses firms financials

Compare a borrower against a peer group for additional transparency



Incorporating Qualitative Overlay Assessment

June 2014

RiskCalc Stress testing – Two different approaches RiskCalc PD&LGD Based Approach

(Granular Modeling) » Access:

— Via Scenario Analyzer or Custom Delivery

» Data: — Credit Research Database (CRD) — Default & Recovery Database (DRD)

» Inputs: — Initial PD & LGD — Sector — Debt type (secured loans, unsecured loans or revolvers) — Macro scenarios — Outstanding Loan Balance — Total Commitment

» Modeling:

» Calibrated on RiskCalc US 4.0 — PD: Forecasting future change based on PD level,

sector and forecasted macro scenarios — LGD: Predict recovery rates based on debt type,

sector, stressed PD levels and macro scenarios

» Output: — Stressed PD & LGD, expected loss, charge offs,

EAD, portfolio balance, usage

RiskCalc Ratio Based Approach (Obligor-Level Modeling)

» Access: — Via RiskCalc Plus website single, batch & XML

» Data:

— Credit Research Database (CRD)

» Inputs: — RiskCalc US 4.0 Corporate Income Statement &

Balance Sheet Inputs

— Macro scenarios

» Modeling:

— Financial ratios are linked to macroeconomic variables

— CCA “credit cycle adjusted” view for forecasted EDFs under stressed scenarios

» Output: — Two years of pro-forma financials

— Baseline EDF and Stressed EDF

Private Firm Commercial & Industrial (C&I) Credit Risk Solutions & Best Practices

Spread, Store, Score, Origination & Stress Testing Needs

Financial Analysis Data Templates in

RiskAnalyst & RiskOrigins software

Data Collection Consistent Single Source

spreading software – RiskAnalyst™ &

RiskOrigins™ software

Scorecards Dual Risk Rating

including PD, LGD & EL

Credit risk scores combined with qualitative factors producing ratings

C&I & CRE Scoring RiskCalc™ Private

Firms

CreditEdge™ Public Firms

Stress Testing Solutions Dashboard Portfolio Reports Stress Testing Models by Asset Class



Questions 5


Thank you


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Economy & Finance

Private Firm Commercial & Industrial Credit Risk Solutions & Best Practices